Abstraction for Analogical Reasoning in Robotic Agents

Analogical reasoning has been implemented in agents to use information about past experiences to guide future action. A similar process for analogical reasoning would enable a robot to reuse past experiences, such as the tasks that an interactive robot could learn from
a human teacher. However, in a robotics domain, analogical reasoning must be performed over the knowledge obtained from sensor input and
must result in actionable output in the form of the robot’s joint configurations. Thus, robotics provides a challenging domain for analogical reasoning, as abstraction plays a necessary role in representing the robot’s task knowledge. We explore the problem of abstraction for a robot which performs analogical reasoning, and provide an example task illustrating how abstraction is necessary for a robot to reason over learned tasks.
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